skysight-inc/HackerNews huggingface dataset:
> The author isn’t talking about implementing an ECS or b-tree here.

Do you mean that b-tree might work here better?

> They’re just populating an array from a file when the program launches, then freeing the whole thing when the program terminates. It’s really not rocket science.

That's exactly why I consider indices.

> since they assumed memory allocated by vec would be pinned (it isn’t)

Could you tell me, please, where you read in the article that I assume it? I wrote in the article "I realized that the problem is related to the fact that vectors of children move in the memory if they don't have enough space to extend." and even made an animation for clarity https://laladrik.xyz/VectorMove.webm. However, if you see the assumption in the article, please, let me know. I correct it or elaborate.
This is a design choice
LLMs don't have metacognition. The biggest problem with them that I've encountered is that they clearly don't understand what they are saying. This means when they are wrong, or shouldn't provide an answer because they don't actually know, it's unlikely to give a good answer. You can easily trick them too because all you're doing is priming a word generator. You can tell them false things are true and then they don't tell the truth anymore. There is no way for them to "realize" something is wrong and correct themselves.
My favorite quote in this topic:

”If intelligence lies in the process of acquiring new skills, there is no task X that solving X proves intelligence”

IMO it especially applies to things like solving a new IQ puzzle, especially when the model is pretrained for that particular task type, like was done with ARC-AGI.

For sure, it’s very good research to figure out what kind of tasks are easy for humans and difficult for ML, and then solve them. The jump in accuracy was surprising. But still in practice the models are unbeliavably stupid and lacking in common sense.

My personal (moving) goalpost for ”AGI” is now set to whether a robot can keep my house clean automatically. Its not general intelligence if it can’t do the dishes. And before physical robots, being less of a turd at making working code would be a nice start. I’m not yet convinced general purpose LLMs will lead to cost-effective solutions to either vs humans. A specifically built dish washer however…
Myopic? You must be under 20 years old. For those of us who have been in tech for over four decades the OPs assessment is exactly the right framing.
And now the Internet is largely a sterile place where the primary business model is built around advertising, selling "data" to "partners", and exploiting gamblers and addicts, while the companies most "saved" from piracy spend hundreds of millions of dollars every year to make increasingly unwatchable, unlistenable, and/or unplayable crap.

I can imagine worse outcomes, but I can certainly imagine better ones too.
Got the same email. Totally shocking.
Less than 100 nanometers?

It always gets me when someone abbreviates nautical miles in lowercase even though context is (almost) always clear it can't be nanometers.
It's Not Sci-Fi–NASA Is Funding These Mind-Blowing Projects
- https://archive.ph/QyKXX
This is also why I avoid them like the plague. I would be a lot less annoyed by the concept of linker scripts if the mechanism were more set up as a concatenative language where you could provide a pile of modifications to behavior that add up to something useful, rather than having to whole-hog replace all of the behavior the compiler starts with (and like, in a world using clang/lld, which supports linker scripts but doesn't internally use them--meaning there is no "default" linker script you can dump/patch--the situation is even worse than it was before... I honestly have a hard time understanding why anyone considers this system acceptable).
I wrote a shell in C for Bruce Molay's Unix System Programming class. I thought the hardest part was getting the globbing, special characters, and escaping to work right. The other element of Unix system programming in C (that some may not be aware of) is that you are expected to make sure every line is 80 char or less. At the end of the day, I was proud of what I accomplished, but I think I only got a _C_ on the assignment.
The outcome of ubiquitous digitalization will vary depending on number of orifices in your crotch. Number of orifices will be verified by medical commission when you reach adulthood. In case of Ukrainian men this ruling likely impacts only those non affluent, their rich kids seem to have a good time in EU and around the world.
I havent been at Bench for over 6 years but it’s always been a business on the verge of failure. The main issue was just the schizophrenic strategy that was being employed. On one hand you have a software company with useful tools and services to automate bookeeping. On the other you had a division of bookeepers that would do a lot of the manual / refinement work. These tasks are at odds with one another. If you automate things you remove power from the teams who are incentivized to scale their org charts. With LLMs, Bench should have been positioned to own this space entirely, and offer a superior or equivalent product with much better margins. Bur they decided to become a services company and not a technology company.

All I can take away from it is a few lessons, because this is a pretty awful outcome for almost every party.
Computers are deterministic. LLMs run on computers. If you use the same seed for the random number generator you’ll see that it will produce the same output given an input.
In general conversation, “intelligence”, “knowledge”, “smartness”, “expertise”, etc are used mostly interchangeably.

If we want to get pedantic, I would point out that “knowledge” is formally defined as “justified true belief”, and I doubt we want to get into the quagmire of whether LLM’s actually have beliefs.

I took OP’s point in the casual meaning, i.e. that LLMs are like what I would call an “intelligent coworker”, or how one might call a Jeopardy game show contestant as intelligent.
Yet YouTube's system takes a lot of time from the time the content is claimed to the time you can escalate to an actual DMCA. In this time your video is down or has its revenue redirected. And if your content gets too many frivolous claims your account is taken down by YouTube's three strike policy before you have finished the dispute process for the first video.

A single button to immediately and without human review or notice period restore the video and monetization, remove the strike, and declare you are willing to solve it in court would solve a lot of the issues with ContentID abuse on YouTube.

Such a system would likely have to sit behind strict identity verification to prevent abuse, and some people wouldn't like that. But that's the price for sharing a platform with some very blatant infringers
In fairness, I’ve never experienced this using Claude with Cursor.
Yeah I love my laser printer. Actually earlier this year I bought an ink tank printer because I've had to print a number of things in color and have been really pleased with that.

But it's very difficult to beat the cost of a laser printer, especially for infrequent prints.
You're right that WebAuthn was much more public, but passkey was not.

I personally tried to stay apprised of passkey's development. After asking several developers and poking around the best I could, I was told several times that it was being primarily developed behind closed doors for corporate interests, invite-only, and wasn't ready for release. The only information available was the WebAuthn forums.

Even now the documentation is still poor, and there's essentially no rationale to understand design and architectural decisions. We're just given a spec and expected to adhere to it.
A little humility would do us good regardless, because we don't know what intelligence is and what consciousness is, we can't truly define it nor do we understand what makes humans conscious and sentient/sapient.

Categorically ruling out intelligence because "it's just a token predictor" puts us at the opposite of the spectrum, and that's not necessarily a better place to be.
Haha fair enough! I use definition to mean essentially "what one sees as the meaning of a word". That "one" could be Webster or another dictionary. Or an individual.

Your definition, is "the meaning of a word as specified in a dictionary".

There's definitely variance between these two definitions, but I think it's a small variance, and we can largely still understand each other despite the variance. I would say that word has converged on a meaning. We could, if we decided it was necessary, create separate words, one to mean an exact definition as defined by an authoritative source, and one to mean how every individual defines a word. But English society has decided that distinction isn't worth creating a new word at this time.

My argument is that introvert/extrovert have much more variance. When some people use the term, they use it to describe a behaviour, a temporary state, a constant personality trait, a spectrum, two perpendicular dimensions, a label on a loose set of behaviours, social skills, energy levels -- it's got so many facets! And dictionaries follow human usage -- so how is a dictionary supposed to pin this down to one or two concise definitions? That's my argument for why the terms are too variant to be exceptionally useful/meaningful.
Just like the strategic announcement of bad news....on a Friday.
> Apple are using their own servers for inference, that's the whole private cloud compute thing.

Not for everything, though. Any ChatGPT/OpenAI-based inference request is being sent to Nvidia GPUs that run models too large for even the biggest Mac servers. You cannot refute this simply because Apple does not sell DGX-like server products. Even the rackmount Apple Silicon is still orders-of-magnitude off on the kind of performance you can get from a 1u GPU rack.

> Looks competitive to me.

When compared on equal grounds, Apple doesn't even have a GPU that beats Nvidia's 30XX series on power efficiency: https://browser.geekbench.com/opencl-benchmarks

If it "looks competitive" to you, then I invite you to look closer than just qualitative evidence. Apple's 3nm desktop designs are losing in straight-shot comparisons with Nvidia's 8nm products.
Ah, the American burger institute strikes again.
I also use it for plant care tips. What should I feed this plant and what kind of soil to use and all the questions I never bothered to Google and crawl through some long blog article on
A Founder's Midlife Crisis
I was inspired to write this because I find that it’s (understandably) rare that people open up about their struggles when talking about their startups and I’ve really appreciated it in the past, so I wanted to share a bit of my journey in return.
Let’s just wait and see - what good can come from endless speculation and what ifs and trying to predict the future?
Are you still a coder when you use libraries or frameworks? You didn't write the code yourself, you're just outsourcing it.
Lying means stating things as facts despite knowing or believing that they are false. I don’t think this accurately characterizes LLMs. It’s more like a fever dream where you might fabulate stuff that appears plausibly factual in your dream world.
Any tips on finding or inventing that meaning?
Title should be "87.4% of world’s population experienced a decline in freedom from 2020 to 2022"

"World's" in there is pretty important.
Sales tax calculations maybe?
eventually as the quality of the public internet’s training corpus declines, we’ll get there
They're an ad company at the end of the day; they still want their cut of the monetization.

Additionally both sides of the dispute normally still want the monetization, they just disagree about who gets the proceeds. And because of the time value curve of YouTube videos (most make something like 90% of their revenue in the first few days), demonetizing has a good chance fo essentially erasing the revenue of the video for the creator.
This rings true for me. I have an autistic daughter and I'm not nearly as severe as she is. But social interactions stress me out, even though I enjoy interacting with people when I have the energy for it. Add to that my sensitivities to sound, light, taste, touch and smell, and it's pretty clear that I am at least on the spectrum. It may be just a label, but helps me make sense of many of my quirks.
Im based here in the US, and unfortunately i signed up yesterday after learning about the bookkeeping special...for a small business $1005 is alot...Square (my bank) basically said they cant do anything about it since i autho the payment...Any suggestions?
> exacerbate societal inequalities

Do you have an example of this?
Use Cursor or something similar and feed it documentation as context. Problem solved.
They seem incapable of saying “I don’t know.”
I could be many things on an anecdotal experience. Can't make a study for thid at my level.
> think of how many people have probably dropped out of CS as a major after staying up all night and failing to find a missing semicolon.

... like a dozen? And in 100% cases it's their teacher's fault.
I've been sold AI as if it can do anything. It's being actively sold like a super intelligent independent human that never needs breaks.

And it just isn't that thing. Or, rather, it is super intelligent but lacks any wisdom at all; thus rendering it useless for how it's being sold to me.

>which is at the early adoption stage

I've said this in other places here. LLM's simply aren't at early adoption stage anymore. They're being packaged into literally every saas you can buy. They're a main selling point for things like website builders and other direct to business software platforms.
I find it fascinating that I can achieve about 85-90% of what I need for simple coding projects in my homelab using AI. These projects often involve tasks like scraping data from the web and automating form submissions.

My workflow typically starts with asking ChatGPT to analyze a webpage where I need to authenticate. I guide it to identify the username and password fields, and it accurately detects the credential inputs. I then inform it about the presence of a session cookie that maintains login persistence. Next, I show it an example page with links—often paginated with numbered navigation at the bottom—and ask it to recognize the pattern for traversing pages. It does so effectively.

I further highlight the layout pattern of the content, such as magnet links or other relevant data presented by the CMS. From there, I instruct it to generate a Python script that spiders through each page sequentially, navigates to every item on those pages, and pushes magnet links directly into Transmission. I can also specify filters, such as only targeting items with specific media content, by providing a sample page for the AI to analyze before generating the script.

This process demonstrates how effortlessly AI enables coding without requiring prior knowledge of libraries like beautifulsoup4 or transmission_rpc. It not only builds the algorithm but also allows for rapid iteration. Through this exercise, I assume the role of a manager, focusing solely on explaining my requirements to the AI and conducting a code review.
No mention of refunds for unused months
Maths in the Bathroom
This post mentions for example complex bugs across many services. Other similar posts write about scaling to millions and billions of users, designing novel algorithms... My point is, great engineers often do fairly mundane things, just very, very well.
> we have no way of knowing in advance what the capabilities of current AI systems will be if we are able to scale them by 10x, 100x, 1000x, and more

This doesn’t solve the unpredictability problem.
I've found the best times to build new habits are when my existing habits are already being disrupted. It gives a "clean slate" effect where I can change things as radical as my sleep schedule, my eating habits, what have you when the old habits are already disrupted. In this way, the New Year absolutely fits the bill: the hectic holiday season, at least for me, disrupts all of my normal rhythms of day to day life, and I find myself yearning in January for a good amount of boredom, at least for awhile.

I also use things like business trips that have me away from home for a week, or planned vacations. Beforehand I get whatever I want ready, be it a home workout routine, a gym membership, what have you and then once all my routines are out of sorts, I can leap straight into a new set of them with seemingly far less friction.

YMMV of course.
That's not what they did at all.
I think this underestimates the degree to which the people on these companies legitimately believe what they’re saying. I’ve worked at one of these companies and absolutely would fall into your category of being a true believer at the time.

People of all stripes are extremely willing to embrace ideas that justify their own personal benefit. A rich person might be more likely to believe in trickle-down economics because ultimately it enriches them — but that doesn’t mean that it’s necessarily a false belief. An American might sincerely believe that gun proliferation is safe, because the information they process is filtered by their biases as it’s important to their cultural identity.

So when your stock options will pay out big from the company’s success, or even just if your paycheque depends on it — you’re more likely to process information and ideas though the lens of your bias. It’s not just being a gullible true believer tricked by the company’s elite — you’re also just willing to interpret it the same way in no small part because it benefits you.
Humans are not machines , they have both rights that machines do not have and also responsibilities and consequences that machines will not have, for example bad driving will cost you money, injury , prison time or even death.

Therefore AI has to be much better than humans at the task to be considered ready to be a replacement.

——

Today robot taxis can only work in fair weather conditions in locations that are planned cities. No autonomous driving system can drive in Nigeria or India or even many european cities that were never designed for cars any time soon .

Working in very specific scenarios is useful , but hardly measure of their intelligence or candidate for replacing humans for the task
FWIW, Newegg is, in fact, where I got mine. It was definitely a matter of waiting some time to see it get in stock. If theres a way to track historical restocking rates/timing on newegg & other sites, that would be really handy to hae, but I haven't looked into it. That's definitely a real supply problem if you're not a big integrator ordering items by the pallet.
LLMs are algorithms written by humans. "AI" is supposed to be a vague term, and not synonymous with one particular implementation.
I hear people say this kind of thing but it confuses me.

1. What does inherit limitations mean?

2. How do we know something is an inherit limitation

3. Is it a problem if arguments for a particular inherit limitation also apply to humans?

From what I’ve seen people will often say things like AI can’t be creative because it’s just a statistical machine, but humans are also “just” statistical machines. People might mean something like humans are more grounded because humans react not just to how the world already works but how the world reacts to actions they take, but this difference misunderstands how LLMs are trained. Like humans LLMs get most of their training from observing the world, but LLMs are also trained with re-enforcement learning and this will surely be an active area of research.
Bada boom! Correctamundo!
Ask them for prorated refund. You paid for a service and didn't receive it.
The unreliability of LLMs is mostly unrelated to their (artificially injected) non-determinism.
I'm woefully uneducated but I think it's a red herring. It does not matter what their vision of AGI is if they are just going to be peddling LLMs as a service to customers.
So there's really no legal recourse if a 501c3 markets itself as a research institute / OSS developer, collects $130MM, and then uses those donations to seed a venture-backed company with closed IP in which the donors get no equity? One that even competes with some of the donors?
MetaMine - It mixes declarative and imperative code with sane rules. It's the most powerful programming language I've ever seen. Unfortunately, it only briefly winked into existence a few years ago. Here's the demo[1] via the Internet Archive to whet your appetite. See also, mentions here on HN[2].

[1] http://web.archive.org/web/20201014024057/https://www.youtub...

[2] https://hn.algolia.com/?dateRange=all&page=0&prefix=false&qu...

[2]
Why not ignore the hype, and just quietly use what works?

I don’t use anything other than ChatGPT 4o and Claude Sonnet 3.5v2. That’s it. I’ve derived great value from just these two.

I even get wisdom from them too. I use them to analyze news, geopolitics, arguments around power structures, urban planning issues, privatization pros and cons, and Claude especially is able to give me the lay of the land which I am usually able to follow up on. This use case is more of the “better Google” variety rather than task-completion, and it does pretty well for the most part. Unlike ChatGPT, Claude will even push back when I make factually incorrect assertions. It will say “Let me correct you on that…”. Which I appreciate.

As long as I keep my critical thinking hat on, I am able to make good use of the lines of inquiry that they produce.

Same caveat applies even to human-produced content. I read the NYTimes and I know that it’s wrong a lot, so I have to trust but verify.
I've been enjoying tree-sitter, using it for a larger project now. I found it more intuitive than peg, but certainly comes with more... weight (entire subfolder, bindings, build process etc) and is more verbose. But can simplify syntax highlighting and other editor related things. Zed uses it extensively, and all you need are queries for each of the features (https://zed.dev/docs/extensions/languages)

Miette is responsible for actually rendering the error messages you see- which was new to me, but have always appreciated how that style of error looks and is so much more helpful than "[1:23]"
he has actually cashed out and donated small amounts (30k or so) at least a couple of times already.
on social media some employees are saying it's insolvent
Metal is neither a general-purpose compute API or a known example of Apple supporting it. If you consider Metal "general purpose" then so is DirectX or even Playstation and Nintendo shaders simply because you can sum matrices.

Accelerate framework might be what you're looking for here, but by most accounts that hasn't improved very much recently. And even still, Accelerate is no analog for OpenCL or CUDA.